{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kats 101 - Basics\n",
    "\n",
    "Kats (**K**its to **A**nalyze **T**ime **S**eries) is a light-weight, easy-to-use, extenable, and generalizable framework to perform time series analysis in Python.  Time series analysis is an essential component of data science and engineering work.  Kats aims to provide a one-stop shop for techniques for univariate and multivariate time series including:\n",
    "\n",
    "1. Forecasting  \n",
    "2. Anomaly and Change Point Detection  \n",
    "3. Feature Extraction \n",
    "\n",
    "\n",
    "and after introducing the basic Kats data structure, this Kats 101 notebook provides a basic introduction to each of these time series techniques in Kats.  The complete table of contents for Kats 101 is as follows: \n",
    "\n",
    "1. Kats Basics          \n",
    "    1.1 Initiate `TimeSeriesData` Object         \n",
    "    1.2 `TimeSeriesData` built-in operations         \n",
    "2. Forecasting with Kats         \n",
    "    2.1 An example with Prophet model         \n",
    "    2.2 An example with Theta model         \n",
    "3. Detection with Kats         \n",
    "    3.1 What are the algorithms?         \n",
    "    3.2 An example with outlier detection method         \n",
    "    3.3 An example with CUSUM algorithm         \n",
    "4. Feature extraction with Kats         \n",
    "5. Summary         "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** We provide two types of tutorial notebooks\n",
    "- **Kats 101**, basic data structure and functionalities in Kats (this tutorial)  \n",
    "- **Kats 20x**, advanced topics, including advanced forecasting techniques, advanced detection algorithms, `TsFeatures`, meta-learning, etc. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Kats Basics\n",
    "`TimeSeriesData` is the basic data structure in Kats to represented univariate and multivariate time series.  There are two ways to initiate it, henceforth referred to as \"Method 1\" and \"Method 2\":\n",
    "\n",
    "1) `TimeSeriesData(df)`, where `df` is a `pd.DataFrame` object with a \"time\" column and any number of value columns.\n",
    "\n",
    "2) `TimeSeriesData(time, value)`, where `time` is either a `pd.Series` or `pd.DatetimeIndex` object and `value` is either a `pd.Series` (for univariate) or a `pd.DataFrame` (for multivariate)\n",
    "\n",
    "## 1.1 Initiate `TimeSeriesData` object\n",
    "We will use the `air_passenger` and `multi_ts` datasets to demonstrate how to create a `TimeSeriesData` object for univariate and multivariate time series, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "# For Google Colab:\n",
    "!pip install kats\n",
    "!wget https://raw.githubusercontent.com/facebookresearch/Kats/main/kats/data/air_passengers.csv\n",
    "!wget https://raw.githubusercontent.com/facebookresearch/Kats/main/kats/data/multi_ts.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime, timedelta\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "sys.path.append(\"../\")\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from kats.consts import TimeSeriesData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01-01</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02-01</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         time  value\n",
       "0  1949-01-01    112\n",
       "1  1949-02-01    118\n",
       "2  1949-03-01    132\n",
       "3  1949-04-01    129\n",
       "4  1949-05-01    121"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "try: # If running on Jupyter\n",
    "    air_passengers_df = pd.read_csv(\"../kats/data/air_passengers.csv\")\n",
    "except FileNotFoundError: # If running on colab\n",
    "    air_passengers_df = pd.read_csv(\"air_passengers.csv\")\n",
    "\n",
    "\"\"\"\n",
    "Note: If the column holding the time values is not called \"time\", you will want to specify \n",
    "the name of this column using the time_col_name parameter in the TimeSeriesData constructor.\n",
    "\"\"\"\n",
    "air_passengers_df.columns = [\"time\", \"value\"]\n",
    "air_passengers_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-03-12</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>53.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-03-13</td>\n",
       "      <td>0.000</td>\n",
       "      <td>53.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-03-14</td>\n",
       "      <td>0.178</td>\n",
       "      <td>53.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-03-15</td>\n",
       "      <td>0.339</td>\n",
       "      <td>53.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-03-16</td>\n",
       "      <td>0.373</td>\n",
       "      <td>53.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         time     v1    v2\n",
       "0  2017-03-12 -0.109  53.8\n",
       "1  2017-03-13  0.000  53.6\n",
       "2  2017-03-14  0.178  53.5\n",
       "3  2017-03-15  0.339  53.5\n",
       "4  2017-03-16  0.373  53.4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "try: # If running on Jupyter\n",
    "    multi_ts_df = pd.read_csv(\"../kats/data/multi_ts.csv\", index_col=0)\n",
    "except FileNotFoundError: # If running on colab\n",
    "    multi_ts_df = pd.read_csv(\"multi_ts.csv\", index_col=0)\n",
    "multi_ts_df.columns = [\"time\", \"v1\", \"v2\"]\n",
    "multi_ts_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we construct `TimeSeriesData` objects for each time series using Method 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "air_passengers_ts = TimeSeriesData(air_passengers_df)\n",
    "multi_ts = TimeSeriesData(multi_ts_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'kats.consts.TimeSeriesData'>\n",
      "<class 'kats.consts.TimeSeriesData'>\n"
     ]
    }
   ],
   "source": [
    "# check that the type of the data is a \"TimeSeriesData\" object for both cases\n",
    "print(type(air_passengers_ts))\n",
    "print(type(multi_ts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "# For the air_passengers TimeSeriesData, check that both time and value are pd.Series\n",
    "print(type(air_passengers_ts.time))\n",
    "print(type(air_passengers_ts.value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "# For the multi_ts TimeSeriesData, time is a pd.Series and value is a pd.DataFrame\n",
    "print(type(multi_ts.time))\n",
    "print(type(multi_ts.value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we show how to construct the same `TimeSeriesData` objects as before using Method 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "air_passengers_ts_from_series = TimeSeriesData(time=air_passengers_df.time, value=air_passengers_df.value)\n",
    "multi_ts_from_series = TimeSeriesData(time=multi_ts_df.time, value=multi_ts_df[['v1', 'v2']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`TimeSeriesData` can accomodate time expressed as a variety of different types, including \n",
    "- standard `datetime`, \n",
    "- `pandas.Timestamp`,\n",
    "- a `str` (if in a non-standard format or if efficiency is important, use the optional `date_format` argument),\n",
    "- `int` (i.e. unix time).\n",
    "\n",
    "Here is an example of how to construct a `TimeSeriesData` object when time is provided in unix time format.\n",
    "\n",
    "\n",
    "Here's an example where the time is auto-interpreted from a unix time format. Using this format just requires a couple optional parameters in the `TimeSeriesData` constructor:\n",
    "- `use_unix_time = True`\n",
    "- `unix_time_units=\"s\"` (the default is `\"ns\"`, indicating nanoseconds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     -662662800.0\n",
       "1     -659980800.0\n",
       "2     -657561600.0\n",
       "3     -654883200.0\n",
       "4     -652291200.0\n",
       "          ...     \n",
       "139   -297190800.0\n",
       "140   -294512400.0\n",
       "141   -291916800.0\n",
       "142   -289238400.0\n",
       "143   -286646400.0\n",
       "Name: time, Length: 144, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dateutil import parser\n",
    "from datetime import datetime\n",
    "\n",
    "# Convert time from air_passengers data to unix time\n",
    "air_passengers_ts_unixtime = air_passengers_df.time.apply(\n",
    "    lambda x: datetime.timestamp(parser.parse(x))\n",
    ")\n",
    "\n",
    "air_passengers_ts_unixtime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01-01 07:00:00</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02-01 08:00:00</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03-01 08:00:00</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04-01 08:00:00</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05-01 08:00:00</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1960-08-01 07:00:00</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1960-09-01 07:00:00</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1960-10-01 08:00:00</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1960-11-01 08:00:00</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1960-12-01 08:00:00</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   time  value\n",
       "0   1949-01-01 07:00:00    112\n",
       "1   1949-02-01 08:00:00    118\n",
       "2   1949-03-01 08:00:00    132\n",
       "3   1949-04-01 08:00:00    129\n",
       "4   1949-05-01 08:00:00    121\n",
       "..                  ...    ...\n",
       "139 1960-08-01 07:00:00    606\n",
       "140 1960-09-01 07:00:00    508\n",
       "141 1960-10-01 08:00:00    461\n",
       "142 1960-11-01 08:00:00    390\n",
       "143 1960-12-01 08:00:00    432\n",
       "\n",
       "[144 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the TimeSeriesData object with the extra arguments to accomodate unix time \n",
    "ts_from_unixtime = TimeSeriesData(\n",
    "        time=air_passengers_ts_unixtime, \n",
    "        value=air_passengers_df.value, \n",
    "        use_unix_time=True, \n",
    "        unix_time_units=\"s\"\n",
    ")\n",
    "\n",
    "ts_from_unixtime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 `TimeSeriesData` built-in operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `TimeSeriesData` object supports many of the same operations of the standard `pandas.DataFrame`, including:\n",
    "- Slicing\n",
    "- Math Operations\n",
    "- Extend \n",
    "- Plotting\n",
    "- Utility Functions (`to_dataframe`, `to_array`, `is_empty`, `is_univariate`)\n",
    "\n",
    "We give examples of each as follows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-02-01</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time  value\n",
       "0 1949-02-01    118\n",
       "1 1949-03-01    132\n",
       "2 1949-04-01    129\n",
       "3 1949-05-01    121"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_ts[1:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Math operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>1949-02-01</td>\n",
       "      <td>236</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time  value\n",
       "0 1949-02-01    236\n",
       "1 1949-03-01    264\n",
       "2 1949-04-01    258\n",
       "3 1949-05-01    242"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_ts[1:5] + air_passengers_ts[1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Equality and Inequality are also supported:\n",
    "\n",
    "air_passengers_ts == air_passengers_ts_from_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_ts == multi_ts_from_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "144"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# length\n",
    "\n",
    "len(air_passengers_ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01-01</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02-01</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1949-06-01</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1949-07-01</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time  value\n",
       "0 1949-01-01    112\n",
       "1 1949-02-01    118\n",
       "2 1949-03-01    132\n",
       "3 1949-04-01    129\n",
       "4 1949-05-01    121\n",
       "5 1949-06-01    135\n",
       "6 1949-07-01    148"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating two slices\n",
    "ts_1 = air_passengers_ts[0:3]\n",
    "ts_2 = air_passengers_ts[3:7]\n",
    "\n",
    "ts_1.extend(ts_2)\n",
    "ts_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# Must pass the name of the value columns to plot\n",
    "air_passengers_ts.plot(cols=['value'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can plot multiple time series from multi_ts by passing in the name of each value column we want to plot\n",
    "multi_ts.plot(cols=['v1','v2'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utility functions\n",
    "\n",
    "Here we provide examples of a few useful Kats utility functions for `TimeSeriesData`.  They can be helpful for working with external libraries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert to `pandas.DataFrame`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01-01</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02-01</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time  value\n",
       "0 1949-01-01    112\n",
       "1 1949-02-01    118\n",
       "2 1949-03-01    132\n",
       "3 1949-04-01    129\n",
       "4 1949-05-01    121"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_ts.to_dataframe().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert to `numpy.ndarray`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[Timestamp('1949-01-01 00:00:00'), 112],\n",
       "       [Timestamp('1949-02-01 00:00:00'), 118],\n",
       "       [Timestamp('1949-03-01 00:00:00'), 132],\n",
       "       [Timestamp('1949-04-01 00:00:00'), 129],\n",
       "       [Timestamp('1949-05-01 00:00:00'), 121]], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_ts.to_array()[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check basic characteristics of the time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_ts.is_empty()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_passengers_ts.is_univariate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_ts.is_univariate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Forecasting with Kats\n",
    "\n",
    "We currently support the following 10 base forecasting models: \n",
    "\n",
    "1. Linear  \n",
    "2. Quadratic   \n",
    "3. ARIMA   \n",
    "4. SARIMA   \n",
    "5. Holt-Winters   \n",
    "6. Prophet   \n",
    "7. AR-Net   \n",
    "8. LSTM   \n",
    "9. Theta   \n",
    "10. VAR   \n",
    "\n",
    "\n",
    "Each models follows the `sklearn` model API pattern:  we create an instance of the model class and then call its `fit` and `predict` methods.  In this section, we provide examples for the Prophet and Theta models.  A more in-depth introduction to forecasting in Kats is provided in the Kats 201 tutorial.\n",
    "\n",
    "\n",
    "\n",
    "## 2.1 An example with Prophet model\n",
    "\n",
    "We will demonstrate how to use Prophet model to forecast with the `air_passengers` data set. Note: this example requires that fbprophet be installed (for example, `pip install kats[prophet]` or `pip install kats[all]`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    }
   ],
   "source": [
    "# import the param and model classes for Prophet model\n",
    "from kats.models.prophet import ProphetModel, ProphetParams\n",
    "\n",
    "# create a model param instance\n",
    "params = ProphetParams(seasonality_mode='multiplicative') # additive mode gives worse results\n",
    "\n",
    "# create a prophet model instance\n",
    "m = ProphetModel(air_passengers_ts, params)\n",
    "\n",
    "# fit model simply by calling m.fit()\n",
    "m.fit()\n",
    "\n",
    "# make prediction for next 30 month\n",
    "fcst = m.predict(steps=30, freq=\"MS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>fcst</th>\n",
       "      <th>fcst_lower</th>\n",
       "      <th>fcst_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1961-01-01</td>\n",
       "      <td>451.152463</td>\n",
       "      <td>438.031293</td>\n",
       "      <td>464.303305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961-02-01</td>\n",
       "      <td>432.553525</td>\n",
       "      <td>419.737356</td>\n",
       "      <td>444.677571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1961-03-01</td>\n",
       "      <td>491.921076</td>\n",
       "      <td>477.773706</td>\n",
       "      <td>505.889436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1961-04-01</td>\n",
       "      <td>494.501049</td>\n",
       "      <td>481.383321</td>\n",
       "      <td>507.426762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1961-05-01</td>\n",
       "      <td>503.407884</td>\n",
       "      <td>489.113097</td>\n",
       "      <td>516.624957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time        fcst  fcst_lower  fcst_upper\n",
       "0 1961-01-01  451.152463  438.031293  464.303305\n",
       "1 1961-02-01  432.553525  419.737356  444.677571\n",
       "2 1961-03-01  491.921076  477.773706  505.889436\n",
       "3 1961-04-01  494.501049  481.383321  507.426762\n",
       "4 1961-05-01  503.407884  489.113097  516.624957"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the predict method returns a dataframe as follows\n",
    "fcst.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the results with uncertainty intervals\n",
    "m.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 An example with Theta model\n",
    "\n",
    "\n",
    "We will now use the Theta model to forecast with the `air_passengers` data set.  \n",
    "\n",
    "The Theta Method (Assimakopoulos and Nikolopoulos, 2000) is a univariate forecasting method that fits two Theta lines: 1) a linear interpolation (called the `Theta=0`) and 2) a second-order difference (called the `Theta=2` line), and then combines them to build a forecast.  Prior to running this forecast, we test the time series for seasonality, deseaonalize if seasonality is detected, and then reseasonalize the calculated forecasts.  \n",
    "\n",
    "Hyndman and Billah (2003) showed that the Theta Method is equivalent to simple exponential smoothing with drift.  In Kats we use this underlying model to calculate prediction intervals for `ThetaModel`.\n",
    "\n",
    "Our implementation of `ThetaModel` in Kats is similar to the [thetaf function in R](https://pkg.robjhyndman.com/forecast/reference/thetaf.html).  Because each of our time series models follow the `sklearn` model API pattern, the code using `ThetaModel` is quite similar to the example above using \n",
    "`ProphetModel`: we initialize the model with its parameters and then call the `fit` and `predict` methods.  We can then use the `plot` method to visualize our forecast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import param and model from `kats.models.theta`\n",
    "from kats.models.theta import ThetaModel, ThetaParams\n",
    "\n",
    "# create ThetaParam with specifying seasonality param value\n",
    "params = ThetaParams(m=12)\n",
    "\n",
    "# create ThetaModel with given data and parameter class\n",
    "m = ThetaModel(data=air_passengers_ts, params=params)\n",
    "\n",
    "# call fit method to fit model\n",
    "m.fit()\n",
    "\n",
    "# call predict method to predict the next 30 steps\n",
    "res = m.predict(steps=30, alpha=0.2)\n",
    "\n",
    "# visualize the results\n",
    "m.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Detection with Kats\n",
    "\n",
    "Kats provides a set of models and algorithms to detect outliers, change points, and trend changes in time series data.\n",
    "\n",
    "\n",
    "## 3.1 What are the algorithms?\n",
    "\n",
    "To detect a specific pattern, we provided different algorithms, which is summarized as follows.\n",
    "- **Outlier Detection**. This usually refers to a abnormal spike in a time series data, which can be detected with `OutlierDetector`\n",
    "- **Change Point Detection**. This refers to a sudden change that the time series have different statistical properties before and after the change. We provided three major algorithms to detect such patterns:\n",
    "    - CUSUM Detection\n",
    "    - Bayesian Online Change Point Detection (BOCPD)\n",
    "    - Stat Sig Detection\n",
    "- **Trend Change Detection**. This refers to a slow trend change on the time series data, which can be detected with Mann-Kendall detection algorithm, `MKDetector`\n",
    "\n",
    "In this tutorial, we will demonstrate the usage of two detectors: `OutlierDetector` and `CUSUM`.  A more in-depth introduction to detection in Kats is provided in the Kats 202 tutorial.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 An example with outlier detection method\n",
    "\n",
    "We provide the `OutlierDetector` module to detect outliers in time series.  Since outliers can cause so many problems in downstream processing, it is important to be able to detect them.  `OutlierDetector` also provides functionality to handle or remove outliers once they are found.\n",
    "\n",
    "Our outlier detection algorithm works as follows:\n",
    "\n",
    "- We do a [seasonal decomposition](https://www.statsmodels.org/stable/generated/statsmodels.tsa.seasonal.seasonal_decompose.html) of the input time series with additive or multiplicative decomposition as specified (default is additive)\n",
    "- We generate a residual time series by either removing only trend or both trend and seasonality if the seasonality is strong.\n",
    "- We detect points in the residual which are outside 3 times the inter quartile range.  This multiplier can be tuned using the `iqr_mult` parameter in `OutlierDetector`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our example below copies the `air_passengers` data set and manually inserts outliers into it. We then use `OutlierDetector` to find them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# deep copy the air_passenger_df \n",
    "air_passengers_outlier_df = air_passengers_df.copy(deep=True)\n",
    "\n",
    "# manually add outlier on the date of '1950-12-01'\n",
    "air_passengers_outlier_df.loc[air_passengers_outlier_df.time == '1950-12-01','value']*=5\n",
    "# manually add outlier on the date of '1959-12-01'\n",
    "air_passengers_outlier_df.loc[air_passengers_outlier_df.time == '1959-12-01', 'value']*=4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the raw data\n",
    "air_passengers_outlier_df.plot(x='time', y='value', figsize=(15,8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform the outlier data into `TimeSeriesData` Object\n",
    "air_passengers_outlier_ts = TimeSeriesData(air_passengers_outlier_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kats.detectors.outlier import OutlierDetector\n",
    "\n",
    "ts_outlierDetection = OutlierDetector(air_passengers_outlier_ts, 'additive') # call OutlierDetector\n",
    "ts_outlierDetection.detector() # apply OutlierDetector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we look at the outliers that the algorithum found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Timestamp('1950-12-01 00:00:00'),\n",
       " Timestamp('1959-11-01 00:00:00'),\n",
       " Timestamp('1959-12-01 00:00:00')]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_outlierDetection.outliers[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After detecting the outlier, we can now easily removal them from the data. Here we will explore two options: \n",
    "- **No Interpolation**: outlier data points will be replaced with **NaN** values\n",
    "- **With Interpolation**: outlier data points will be replaced with **linear interploation** values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "air_passengers_ts_outliers_removed = ts_outlierDetection.remover(interpolate = False) # No interpolation\n",
    "air_passengers_ts_outliers_interpolated = ts_outlierDetection.remover(interpolate = True) # With interpolation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we visualize the difference between these two approaches to removing outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,8), nrows=1, ncols=2)\n",
    "\n",
    "air_passengers_ts_outliers_removed.to_dataframe().plot(x = 'time',y = 'y_0', ax= ax[0])\n",
    "ax[0].set_title(\"Outliers Removed : No interpolation\")\n",
    "air_passengers_ts_outliers_interpolated.to_dataframe().plot(x = 'time',y = 'y_0', ax= ax[1])\n",
    "ax[1].set_title(\"Outliers Removed : With interpolation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 An example with CUSUM algorithm\n",
    "\n",
    "Cusum is a method to detect an up/down shift of means in a time series. Our implementation has two main steps:\n",
    "\n",
    "1.  **Locate the change point:** This is an iterative process where we initialize a change point (in the middle of the time series) and CUSUM time series based on this change point.  The next changepoint is the location where the previous CUSUM time series is maximized (or minimized).  This iteration continues until either 1) a stable changepoint is found or 2) we exceed the limit number of iterations.\n",
    "\n",
    "2.  **Test the change point for statistical significance:** Conduct log likelihood ratio test to test if the mean of the time series changes at the changepoint calculated in Step 1.  The null hypothesis is that there is no change in mean.\n",
    "\n",
    "By default, we report a detected changepoint if and only if we reject the null hypothesis in Step 2.  \n",
    "\n",
    "Here are a few additional points worth mentioning:\n",
    "- We assume there is at most one increase change point and at most one decrease change point.  You can use the `change_directions` argument in the detector to specify whether you are looking an increase, a decrease, or both (default is both).\n",
    "- We use Gaussian distribution as the underlying model to calculate the CUSUM time series value and conduct the hypothesis test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import packages\n",
    "from kats.consts import TimeSeriesData, TimeSeriesIterator\n",
    "from kats.detectors.cusum_detection import CUSUMDetector\n",
    "\n",
    "# synthesize data with simulation\n",
    "np.random.seed(10)\n",
    "df = pd.DataFrame(\n",
    "    {\n",
    "        'time': pd.date_range('2019-01-01', '2019-03-01'),\n",
    "        'increase':np.concatenate([np.random.normal(1,0.2,30), np.random.normal(2,0.2,30)]),\n",
    "        'decrease':np.concatenate([np.random.normal(1,0.3,50), np.random.normal(0.5,0.3,10)]),\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is straightforward to use `CUSUMDetector` to detect an increase or a decrease when there is one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# detect increase\n",
    "timeseries = TimeSeriesData(\n",
    "    df.loc[:,['time','increase']]\n",
    ")\n",
    "detector = CUSUMDetector(timeseries)\n",
    "\n",
    "# run detector\n",
    "change_points = detector.detector(change_directions=[\"increase\"])\n",
    "\n",
    "# plot the results\n",
    "plt.xticks(rotation=45)\n",
    "detector.plot(change_points)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# detect decrease\n",
    "timeseries = TimeSeriesData(\n",
    "    df.loc[:,['time','decrease']]\n",
    ")\n",
    "detector = CUSUMDetector(timeseries)\n",
    "\n",
    "# run detector\n",
    "change_points = detector.detector(change_directions=[\"decrease\"])\n",
    "\n",
    "# plot the results\n",
    "plt.xticks(rotation=45)\n",
    "detector.plot(change_points)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we try to detect a decrease in a series where there is only an increase, no change point will be found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:No change points detected!\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# detect increase\n",
    "timeseries = TimeSeriesData(\n",
    "    df.loc[:,['time','increase']]\n",
    ")\n",
    "detector = CUSUMDetector(timeseries)\n",
    "\n",
    "# run detector\n",
    "change_points = detector.detector(change_directions=[\"decrease\"])\n",
    "\n",
    "# plot the results\n",
    "plt.xticks(rotation=45)\n",
    "detector.plot(change_points)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we do not specify which change directions we are looking for, `CUSUMDetector` will look for both increases and decreases.  In the case below where there is only an increase, it will detect that increase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# detect increase\n",
    "timeseries = TimeSeriesData(\n",
    "    df.loc[:,['time','increase']]\n",
    ")\n",
    "detector = CUSUMDetector(timeseries)\n",
    "\n",
    "# run detector\n",
    "change_points = detector.detector()\n",
    "\n",
    "# plot the results\n",
    "plt.xticks(rotation=45)\n",
    "detector.plot(change_points)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Feature Extraction with Kats\n",
    "\n",
    "We provide the `TsFeatures` module to calculate a set of meaningful features for a time series, including:\n",
    "- STL (Seasonal and Trend decomposition using Loess) Features\n",
    "    - Strength of Seasonality\n",
    "    - Strength of Trend\n",
    "    - Spikiness\n",
    "    - Linearity\n",
    "- Amount of Level Shift\n",
    "- Presence of Flat Segments\n",
    "- ACF and PACF Features\n",
    "- Hurst Exponent\n",
    "- ARCH Statistic\n",
    "\n",
    "Given a collection of time series, these features can be used to identify specific series that are similar or outlying.  Our `TsFeatures` module is similar to the one that is [freely available in R](https://pkg.robjhyndman.com/tsfeatures/index.html).\n",
    "\n",
    "These features also play a crucial role in many downstream projects, including \n",
    "1. “Meta-learning”, i.e., choosing the best forecasting model based on characteristics of the input time series \n",
    "2. Time series classification and clustering analysis\n",
    "3. Nowcasting algorithms for better short-term forecasting\n",
    "\n",
    "Now we will show you how to use `TsFeatures` get the features for the `air_passenger` data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initiate feature extraction class\n",
    "from kats.tsfeatures.tsfeatures import TsFeatures\n",
    "tsFeatures = TsFeatures()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_air_passengers = TsFeatures().transform(air_passengers_ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can see the dictionary of features as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'length': 144,\n",
       " 'mean': 280.2986111111111,\n",
       " 'var': 14291.97333140432,\n",
       " 'entropy': 0.4287365561752448,\n",
       " 'lumpiness': 3041164.5629058965,\n",
       " 'stability': 12303.627266589507,\n",
       " 'flat_spots': 2,\n",
       " 'hurst': -0.08023291030513455,\n",
       " 'std1st_der': 27.206287853461966,\n",
       " 'crossing_points': 7,\n",
       " 'binarize_mean': 0.4444444444444444,\n",
       " 'unitroot_kpss': 0.12847508180149445,\n",
       " 'heterogeneity': 126.06450625819339,\n",
       " 'histogram_mode': 155.8,\n",
       " 'linearity': 0.853638165603188,\n",
       " 'trend_strength': 0.9383301875692747,\n",
       " 'seasonality_strength': 0.3299338017939569,\n",
       " 'spikiness': 111.69732482853489,\n",
       " 'peak': 6,\n",
       " 'trough': 3,\n",
       " 'level_shift_idx': 118,\n",
       " 'level_shift_size': 15.599999999999966,\n",
       " 'y_acf1': 0.9480473407524915,\n",
       " 'y_acf5': 3.392072131604336,\n",
       " 'diff1y_acf1': 0.30285525815216935,\n",
       " 'diff1y_acf5': 0.2594591065999471,\n",
       " 'diff2y_acf1': -0.19100586757092733,\n",
       " 'diff2y_acf5': 0.13420736423784568,\n",
       " 'y_pacf5': 1.0032882494015292,\n",
       " 'diff1y_pacf5': 0.21941234780081417,\n",
       " 'diff2y_pacf5': 0.2610103428699484,\n",
       " 'seas_acf1': 0.6629043863684492,\n",
       " 'seas_pacf1': 0.1561695525558896,\n",
       " 'firstmin_ac': 8,\n",
       " 'firstzero_ac': 52,\n",
       " 'holt_alpha': 0.995070674288148,\n",
       " 'holt_beta': 0.0042131109997650676,\n",
       " 'hw_alpha': 0.9999999850988388,\n",
       " 'hw_beta': 6.860710750514223e-16,\n",
       " 'hw_gamma': 1.3205838720422503e-08}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# features_air_passengers\n",
    "features_air_passengers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Summary\n",
    "\n",
    "in this tutorial, we have shown the basic operations of **Kats** in time series application, including basic introductions to forecasting, detection, feature extraction in Kats.\n",
    "\n",
    "If you want to explore these topics in more detail, you can check out Kats 20x tutorials:\n",
    "\n",
    "- [Kats 201 Forecasting with Kats](kats_201_forecasting.ipynb)\n",
    "- [Kats 202 Detection with Kats](kats_202_detection.ipynb)\n",
    "- [Kats 203 Time Series Features](kats_203_tsfeatures.ipynb)\n",
    "- [Kats 204 Forecasting with Meta-Learning](kats_204_metalearning.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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